A short-term traffic volume prediction model is proposed in this study. The model employs a multi-dimensional autoregressive type analysis so that not only the time series of traffic volume data on the study link of the road network but also traffic volume data on other links which may feed into the study link can be incorporated in the model. The similarity of the traffic flow pattern from day to day is taken into account in making up the prediction model. That is, the parameter vector h involved in the model to predict the traffic flow at k time intervals ahead of time is assumed smoothly changing with respect to day and identified by making use of kalman filtering theory.The proposed model is tested and compared against the two existing models, i.e., one utilizing the Box-Jenkins method and another based on spectral analysis, by using data collected from a network in Nagano City. The data are 15-min vehicle counts recorded at intervals of five minutes, the time unit in the models. The test results suggest that the proposed model performs substantially (up to 48% in terms of root mean square error of predictions) better than the existing models except in 5-min prediction. The tests also indicate a robustness in model performance as the prediction horizon increases, thus suggesting desirable behavior for longer term prediction.